Partial pooling for Minnesota radon measurements

Published by Chris Fonnesbeck and contributors / PyMC

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The public work

The notebook joins Minnesota household and county data, fits pooled, unpooled and hierarchical radon models, and displays posterior diagnostics and comparisons. The partial-pooling plots show how estimates change with county sample size.

What to notice

Compare pooling assumptions and inspect uncertainty, especially in small groups; an extreme point estimate is weaker evidence when little data supports it.

Keep the context

Historical observational measurements and specified Bayesian models. Priors, sampling diagnostics and model assumptions matter. The notebook does not assess the current safety of a particular home.

AI use: Not reported in the source.

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A useful public example is not an assessment of a reader, a publisher or a Better Loop member.

Authored practice suggestion

Try the idea.
Check your own work.

Use material you are allowed to work with. This suggestion is preparation; it does not record a completed task or an improvement.

A prompt to adapt privately

Using local copies of the notebook and its public data, compare pooled and partially pooled estimates for counties with different sample sizes. Record priors, transformations and convergence diagnostics. Explain how uncertainty changes the interpretation without turning a group estimate into a claim about an individual household.

A check to adapt

Model comparisons use the same transformed observations; diagnostics and uncertainty intervals are included; small-group estimates are not presented as certain household-level conclusions.

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